Author: joern
Date: Thu Jan 2 10:46:51 2014
New Revision: 1554784
URL: http://svn.apache.org/r1554784
Log:
OPENNLP-624 Training parameters can now be adjusted
Modified:
opennlp/addons/liblinear-addon/LiblinearParams.txt
opennlp/addons/liblinear-addon/src/main/java/LiblinearTrainer.java
Modified: opennlp/addons/liblinear-addon/LiblinearParams.txt
URL:
http://svn.apache.org/viewvc/opennlp/addons/liblinear-addon/LiblinearParams.txt?rev=1554784&r1=1554783&r2=1554784&view=diff
==============================================================================
--- opennlp/addons/liblinear-addon/LiblinearParams.txt (original)
+++ opennlp/addons/liblinear-addon/LiblinearParams.txt Thu Jan 2 10:46:51 2014
@@ -16,5 +16,14 @@
# Sample machine learning properties file
Algorithm=LiblinearTrainer
-Iterations=100
Cutoff=0
+
+# All solver types are listed in the SolverType enum
+solverType = L1R_LR
+
+# The following parameters should be adapted,
+# please refer to the liblinear documentation
+c = 1
+eps = 0.001
+p = 0.1
+bias = 0
Modified: opennlp/addons/liblinear-addon/src/main/java/LiblinearTrainer.java
URL:
http://svn.apache.org/viewvc/opennlp/addons/liblinear-addon/src/main/java/LiblinearTrainer.java?rev=1554784&r1=1554783&r2=1554784&view=diff
==============================================================================
--- opennlp/addons/liblinear-addon/src/main/java/LiblinearTrainer.java
(original)
+++ opennlp/addons/liblinear-addon/src/main/java/LiblinearTrainer.java Thu Jan
2 10:46:51 2014
@@ -27,6 +27,9 @@ import java.util.HashMap;
import java.util.List;
import java.util.Map;
+import opennlp.tools.ml.AbstractEventTrainer;
+import opennlp.tools.ml.model.DataIndexer;
+import opennlp.tools.ml.model.MaxentModel;
import de.bwaldvogel.liblinear.Feature;
import de.bwaldvogel.liblinear.FeatureNode;
import de.bwaldvogel.liblinear.Linear;
@@ -35,20 +38,72 @@ import de.bwaldvogel.liblinear.Parameter
import de.bwaldvogel.liblinear.Problem;
import de.bwaldvogel.liblinear.SolverType;
import de.bwaldvogel.liblinear.Train;
-import opennlp.tools.ml.AbstractEventTrainer;
-import opennlp.tools.ml.model.DataIndexer;
-import opennlp.tools.ml.model.MaxentModel;
public class LiblinearTrainer extends AbstractEventTrainer {
+ private final SolverType solverType;
+ private final Double c;
+ private final Double eps;
+ private final Double p;
+
+ private final int bias;
+
+ // TODO: Is is probably better to specifiy an initalizer method
+ // and throw an exception from it, rather than throwing it here ?!
public LiblinearTrainer(Map<String, String> trainParams,
Map<String, String> reportMap) {
super(trainParams, reportMap);
- // TODO: Extract solver type here
- // depending on it, extract parameters
- // e.g. bias, C, eps for L1_LR
+ String solverTypeName = trainParams.get("solverType");
+
+ if (solverTypeName != null) {
+ try {
+ solverType = SolverType.valueOf(trainParams.get("solverType"));
+ }
+ catch (IllegalArgumentException e) {
+ throw new IllegalArgumentException("solverType [" + solverTypeName +
"] is not available!");
+ }
+ }
+ else {
+ throw new IllegalArgumentException("solverType needs to be specified!");
+ }
+ String cValueString = trainParams.get("c");
+
+ if (cValueString != null) {
+ c = Double.valueOf(cValueString);
+ }
+ else {
+ throw new IllegalArgumentException("c must be specified");
+ }
+
+ // eps
+ String epsValueString = trainParams.get("eps");
+
+ if (epsValueString != null) {
+ eps = Double.valueOf(epsValueString);
+ }
+ else {
+ throw new IllegalArgumentException("eps must be specified");
+ }
+
+ String pValueString = trainParams.get("p");
+
+ if (pValueString != null) {
+ p = Double.valueOf(pValueString);
+ }
+ else {
+ throw new IllegalArgumentException("p must be specified");
+ }
+
+ String biasValueString = trainParams.get("bias");
+
+ if (biasValueString != null) {
+ bias = Integer.valueOf(biasValueString);
+ }
+ else {
+ throw new IllegalArgumentException("eps must be specified");
+ }
}
private static Problem constructProblem(List<Double> vy, List<Feature[]> vx,
int maxIndex, double bias) {
@@ -91,8 +146,6 @@ public class LiblinearTrainer extends Ab
// outcomes
int outcomes[] = indexer.getOutcomeList();
- final int bias = 0;
-
int max_index = 0;
// For each event ...
@@ -123,7 +176,7 @@ public class LiblinearTrainer extends Ab
}
Problem problem = constructProblem(vy, vx, max_index, bias);
- Parameter parameter = new Parameter(SolverType.L1R_LR, 1d, 0.001d);
+ Parameter parameter = new Parameter(solverType, c, eps, p);
Model liblinearModel = Linear.train(problem, parameter);
